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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
Edi Winarko
Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
Computer Science & IT Control & Systems Engineering

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Sentiment Analysis of Movie Opinion in Twitter Using Dynamic Convolutional Neural Network Algorithm Fajar Ratnawati; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 12, No 1 (2018): January
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.19237

Abstract

Movie has unique characteristics. When someone writes an opinions about a movie, not only the story in the movie itself is written, but also the people involved in the movie are also written. Opinion ordinary movie written in social media primarily  twitter.To get a tendency of opinion on the movie, whether opinion is likely  positive, negative or neutral, it takes a sentiment analysis. This study aims to classify the sentiment is positive, negative and neutral from opinions Indonesian language movie and look for the accuracy, precission, recall and f-meausre of the method used is Dynamic Convolutional Neural Network. The test results on a system that is built to show that Dynamic Convolutional Neural Network algorithm provides accuracy results better than Naive Bayes method, the value of accuracy of 80,99%, the value of precission 81,00%, recall 81,00%, f-measure 79,00%   while the value of the resulting accuracy Naive Bayes amounted to 76,21%, precission 78,00%, recall 76,00%, f-measure 75,00%.
Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark Rakhmad Ikhsanudin; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 2 (2019): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.38596

Abstract

Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.
Modification of Stemming Algorithm Using A Non Deterministic Approach To Indonesian Text Wafda Rifai; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 13, No 4 (2019): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.49072

Abstract

 Natural Language Processing is part of Artificial Intelegence that focus on language processing. One of stage in Natural Language Processing is Preprocessing. Preprocessing is the stage to prepare data before it is processed. There are many types of proccess in preprocessing, one of them is stemming. Stemming is process to find the root word from regular word. Errors when determining root words can cause misinformation. In addition, stemming process does not always produce one root word because there are several words in Indonesian that have two possibilities as root word or affixes word, e.g.the word “beruang”.To handle these problems, this study proposes a stemmer with more accurate word results by employing a non deterministic algorithm which gives more than one word candidate result. All rules are checked and the word results are kept in a candidate list. In case there are several word candidates were found, then one result will be chosen.This stemmer has been tested to 15.934 word and results in an accurate level of 93%. Therefore the stemmer can be used to detect words with more than one root word.
Adwords Keyword Set Selection Decision Support System Using AHP and TOPSIS Method Sholikin Ady Chandra; Edi Winarko; Sigit Priyanta
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 2 (2020): April
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.50731

Abstract

CV. Gitani Creative Agency is a company engaged in the field of creative agency providing digital marketing service. Google Adwords is a platform used by the company to run this service. Keyword set selection is critical to the performance of ads. However, finding the right keyword set is not an easy task. The company needs to consider various criteria to get the optimal advertising results. Decision support system (DSS) is needed as an objective reference in the process of keyword set selection. The criteria for decision-making are click, impressions, cost, and avg. CPC.AHP method is used to compare the value of each criteria and then generate priority weights of each criteria. While TOPSIS method is used for alternative ranking. The combination of these methods aims to improve the performance of TOPSIS method.The result of this study shows that the combination of AHP and TOPSIS methods can be used to determine the best keyword set for ads. Based on the testing results, DSS can do alternative ranking correctly in accordance with the results of manual calculation and it is also flexible to the changes in criteria and alternatives.
Aspect-Based Sentiment Analysis on Indonesian Restaurant Review Using a Combination of Convolutional Neural Network and Contextualized Word Embedding Putri Rizki Amalia; Edi Winarko
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 15, No 3 (2021): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.67306

Abstract

Someone's opinion on a product or service that is poured through a review is something that is quite important for the owner or potential customer. However, the large number of reviews makes it difficult for them to analyze the information contained in the reviews. Aspect-based sentiment analysis is the process of determining the sentiment polarity of a sentence based on predetermined aspects.This study aims to analyze an Indonesian restaurant review using a combination of Convolutional Neural Network and Contextualized Word Embedding models. Then it will be compared with a combination of Convolutional Neural Network and Traditional Word Embedding models. The result of aspect-classification on three models; BERT-CNN, ELMo-CNN, and Word2vec-CNN give the best results on the ELMo-CNN model with micro-average precision of 0.88, micro-average recall of 0.84, and micro-average f1-score of 0.86. Meanwhile, the sentiment-classification gives the best results on the BERT-CNN model with a precision value of 0.89, a recall of 0.89, and an f1-score of 0.91. Classification using data without stemming have almost similar results, even better than using data with stemming.
Co-Authors Fajar Ratnawati Putri Rizki Amalia Rakhmad Ikhsanudin Sholikin Ady Chandra Sigit Priyanta Wafda Rifai